#language-models

#language-models

At its core (dare I say heart), AI is a machine of probability. Word by word, it predicts what is most likely to come next. This continuation is dressed up as conversation, but it isn't cognition. It is a statistical trick that feels more and more like thought. Training reinforces the trick through what's called a loss function. But this isn't a pursuit of truth. It measures how well a sequence of words matches the patterns of human language.

Researchers identified 454 overused terms from AI language models, revealing that 13.5 to 40 percent of biomedical article abstracts were likely generated or assisted by AI.

The results for phi-3-mini on standard open-source benchmarks measure the model's reasoning ability, comparing it to phi-2 and several other notable models.

QDyLoRA offers an efficient and effective technique for LoRA-based fine-tuning LLMs on downstream tasks, eliminating the need for tuning multiple models for optimal rank.

The AI industry's quest for high-quality training data has led companies like Anthropic to explore controversial practices in acquiring books for their models.

In sequence labeling tasks, traditional metrics like the F1 score are insufficient. Our study introduces a modified approach to better assess model performance in identifying praise.

"While commercial LLMs incorporate safety mechanisms to block harmful outputs, these safeguards are increasingly proving insufficient. A critical vulnerability lies in jailbreaking..."

As a conversation designer, it's important to understand some of the techniques used to optimize large language models (LLMs).

The 'Best of Summer' inserts in the Chicago Sun-Times and Philadelphia Inquirer included factual inaccuracies signifying the impact of AI in journalism.

Meta's generative AI developer conference, LlamaCon, was to unveil the 'Behemoth' model, but due to development struggles, the release has been postponed, with concerns about its capabilities.